Probing Real Sensory Worlds of Receivers with Unsupervised Clustering

M. Pfeiffer, M. Hartbauer, A. B. Lang, W. Maass, and H. Römer

Abstract:

The task of an organism to extract information about the external environment
from sensory signals is based entirely on the analysis of ongoing afferent
spike activity provided by the sense organs. We investigate the processing of
auditory stimuli by an acoustic interneuron of insects. In contrast to most
previous work we do this by using stimuli and neurophysiological recordings
directly in the nocturnal tropical rainforest, where the insect communicates.
Different from typical recordings in sound proof laboratories, strong
environmental noise from multiple sound sources interferes with the
perception of acoustic signals in these realistic scenarios. We apply a
recently developed unsupervised machine learning algorithm based on
probabilistic inference to find frequently occurring firing patterns in the
response of the acoustic interneuron. We can thus ask how much information
the central nervous system of the receiver can extract from bursts without
ever being told which type and which variants of bursts are characteristic
for particular stimuli. Our results show that the reliability of burst coding
in the time domain is so high that identical stimuli lead to extremely
similar spike pattern responses, even for different preparations on different
dates, and even if one of the preparations is recorded outdoors and the other
one in the sound proof lab. Simultaneous recordings in two preparations
exposed to the same acoustic environment reveal that characteristics of burst
patterns are largely preserved among individuals of the same species. Our
study shows that burst coding can provide a reliable mechanism for acoustic
insects to classify and discriminate signals under very noisy real-world
conditions. This gives new insights into the neural mechanisms potentially
used by bushcrickets to discriminate conspecific songs from sounds of
predators in similar carrier frequency bands.